Are We Ready for AI at the Speed It Demands?
The AI era isn’t about tools. It’s about speed, leadership, and whether your systems—and people—can keep up.
The future isn’t coming—it’s already accelerating. If we’re not rethinking how we learn, build, and lead, we’re already behind.
A week ago, my sister asked me a question that stopped me cold.
“What should her sons be studying—with AI changing everything?”
It was a simple question. But underneath it sat something much bigger. The same question every parent, educator, leader, and policymaker should be asking right now.
Not just about what we’re teaching—but whether we’re ready for the future that’s racing toward us.
From Software to Something Else Entirely
I’ve spent most of my life building software. My first job as a software engineer had me writing code in an era where the rules were clear: input in, logic applied, output out. There was a rhythm to it—linear, understandable, sometimes fast, but rarely chaotic.
But today? The ground has shifted.
Generative AI and large language models (LLMs) have torn up the old rulebook. These aren’t just tools—they’re collaborators. They don’t follow instructions like traditional software; they interpret, generate, and even create. And they’re evolving every week.
The work I started my career doing—coding, debugging, deploying—can increasingly be done by AI-powered teammates. Not in five years. In the next one or two.
And the people building and steering this future? They’re not being subtle about where it’s headed:
“We are, for the first time, supplying the world with an essential, non-durable commodity called intelligence. The world never had this.”
—Satya Nadella, Microsoft CEO
“AI will be doing the work of mid-level engineers this year.”
—Mark Zuckerberg
“AI will replace doctors, teachers, and others within 5 to 10 years. It won’t be necessary to have humans for most things.”
—Bill Gates
“AI is going to be the runtime that is going to shape all of what we do.”
—Satya Nadella, Microsoft CEO
My sister has probably been seeing these headlines too—hearing the buzz, watching the pace pick up—and quietly wondering the same thing a lot of people are: what kind of future are we actually walking into, and how fast is it getting here?
These aren’t throwaway soundbites—they’re flashing red signals. Whether or not you agree with the timelines, the message is clear: we’re on the brink of an acceleration that most institutions—educational, professional, even governmental—aren’t built to handle.This Isn’t Just About Software
Across every industry, the story is the same: AI is showing up with new capabilities, and organizations are trying to catch up in real time.
Healthcare: AI copilots help doctors with diagnostics and patient communication—but many systems are still stuck in the early 2000s.
Pharma: AI is speeding up drug discovery, but the industry still runs on legacy approval processes.
Education: Students are learning with AI tutors—but the curriculum hasn’t caught up, and most schools treat ChatGPT like a cheat code.
Manufacturing: Predictive maintenance and smart automation are real—but most plants still operate on outdated systems.
Finance: AI models are analyzing markets in seconds—but getting anything to production takes months of internal red tape.
The tech is sprinting. Most of us are walking. And that gap is only getting wider.
More Techquity Takes on Changes and AI
A Framework for AI Readiness
Here’s a simplified version of the framework we use at Techquity when helping companies cut through the noise and figure out where AI can actually make a difference:
1. Strategic Fit
How does AI align with your business model and how you compete? Are you optimizing productivity—or rethinking what’s possible?
2. Organizational Readiness
Do your teams have the structure, skills, and alignment to turn AI ambition into execution? Or are you missing critical roles or leadership?
3. Data + Infrastructure Maturity
Do you have the foundation—data, tooling, architecture—to support scalable AI work? Or are you stuck cleaning spreadsheets while others are shipping?
4. Speed of Decision-Making
Can your teams test and iterate fast enough to stay relevant? Or are you locked in planning cycles while the market moves?
5. Trust + Governance
Do your people understand, trust, and use AI? Because without adoption, even great tech doesn’t move the needle.
What Should They Study?
Back to my sister’s question.
She’s not just any parent—she’s a Penn grad, a successful dentist, and a business owner. She understands what it means to build something from the ground up, to adapt, to lead. But like many smart, accomplished people right now, she still thinks of AI mostly as a chat box.
So when she asked, “What should her sons be studying?”, here’s what I told her:
Don’t focus on majors. Focus on mindsets.
They should learn how to:
Solve hard problems.
Communicate clearly.
Adapt fast.
Work with machines—not fear them.
Make hard decisions—and own the outcome.
This isn’t about choosing an easy path. It’s about building the kind of grit and judgment that makes the path easier later. Less play, more rigor and grit. The ones who learn how to think, act, and lead when things are messy? They’re the ones who thrive—no matter what AI throws at us.
But here’s what hit me after that conversation—and it’s something I now say to every executive team I advise:
The same questions my sister is asking about her kids—companies need to be asking about their people.
Are we building the talent, systems, and culture to thrive in an AI-powered world?
Do we have the right skills on our teams?
Are our tools flexible enough to integrate new intelligence?
Are our processes built to learn and adapt—or are they locked in old rhythms?
And most importantly—do our people trust the systems they’re being asked to use?
The future doesn’t arrive with a press release. It shows up in choices like these. Quietly. Urgently.
This isn’t just a tech issue. It’s becoming a defining cultural and leadership question.
This year at TED AI San Francisco, they’re asking what might be the defining question of our time:
“Are We Bold Enough?”
That question—posed in the context of AI—isn’t just about vision. It’s about action.
It’s about the courage to reimagine how we lead, how we build, and
what kind of future we want to shape—before it shapes us.
What We’re Seeing at Techquity
At Techquity, we talk to a lot of CEOs—and here’s the reality:
Some have teams working on AI, but they’re stuck in surface-level experimentation. Prototypes, vendor meetings, internal demos. Plenty of motion, not much momentum.
Others don’t know where to begin. They don’t know what roles they need, how to structure the work, or why their early investments haven’t translated to results.
Most default to AI-enhanced strategies—adding models on top of existing systems to optimize what they already do. It’s safer. Familiar. But as I wrote in “AI-First vs. AI-Enhanced: Which Path Are You On?”, that’s not transformation. That’s incrementalism.
In one recent conversation, a CEO told me her team was technically talented, deeply invested in improving data quality, and doing a lot of the right things—on paper. But they weren’t shipping real product outcomes. They weren’t moving fast, or thinking in terms of linked data, feedback loops, or how AI connects directly to customer value.
Her words: “They’re still in their own world.”
What they needed wasn’t more horsepower. It was technical leadership—someone who could translate the business vision into a clear product and AI roadmap. Someone who could ask:
What are we actually trying to build?
Why does it matter to the customer?
And what’s the shortest path from where we are to that future?
Without that translation layer, even the best teams spin in circles.
And very few organizations are asking the tougher questions:
How do we scale this?
How do we build systems and teams that can evolve with the technology?
How does AI reshape the very core of how we create value?
Those are harder conversations—but they’re the ones that lead to real change.
That’s the work we do at Techquity: helping leaders connect their AI ambition to operational reality—and building the foundation to make it scalable and sustainable.
That’s why we often begin with something we call an AI Leverage Discovery—a fast, focused engagement with leadership teams to identify 2–3 high-impact opportunities, assess readiness, and align on what’s worth building. It’s not a strategy deck. It’s a decision framework—designed to help companies move from ideas to action with confidence.
The Real Question
So when my sister asked me that question, I knew she was really asking something deeper:
What kind of future are we preparing our kids—and our companies—for?
We’ve written before about the difference between being AI-first and AI-enhanced… and about how dabbling with AI without a strong foundation is like digging shallow digital wells and hoping you hit water.
But now it’s not just theory—it’s personal.
It’s not just strategy.
It’s about relevance.
It’s about readiness.
It’s about learning how to lead when things change fast. How to build when the rules are being rewritten. And how to stay human in a world of intelligent machines.
That’s the real work ahead.
Andrew Tahvildary is on the leadership team at www.techquity.ai. He is the primary author of this post. Andrew is a CTO who has led 7 tech startups to successful exits, exceeding $2 billion in total transaction value.
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